Regionalization-Based Low-Flow Estimation for Ungauged Basins in a Large-Scale Watershed

The accurate estimation of low flow is necessary for effective water resource management, especially in regions with limited hydrological data. This study aims to enhance low-flow prediction by developing regional regression models based on climatological variables. Cluster analysis based on Ward’s method and K-means algorithm was applied to delineate hydrologically homogeneous regions within the Nakdong River Basin. Multiple regression models were developed for each cluster to estimate low-flow indicators, Q95 and 7Q. The results demonstrated that regional regression models outperformed the global regression model with log and square-root transformations improving predictive accuracy. Spatial analysis revealed that the key determinants of low-flow estimation may vary across hydrologic conditions, emphasizing the necessity of regionalized approaches for the estimation of low flow due to the limitations of a single global model in heterogeneous watersheds. The proposed methodology is believed to provide a robust framework for hydrological regionalization that can improve the estimation of low flow and support water resource management.